Model Uncertainty under Non-Gaussian Errors: Bayesian Model Averaging and Selection in Stochastic Frontier Models
Choosing the right economic model when errors don't follow standard patterns
When economists measure how efficiently firms operate, they typically assume errors follow a bell curve—but real data often doesn't. This paper shows that using specialized statistical methods that account for skewed, non-normal errors can change which economic models researchers should choose and how confident they should be in their conclusions. The effect is strongest when measuring efficiency differences is most important.
Efficiency analysis affects major decisions: regulators use it to assess utility companies and hospitals, investors use it to value firms, and governments use it to benchmark public agencies. Using the wrong statistical assumptions can lead to systematically incorrect efficiency rankings. This work shows researchers can now check whether their choice of statistical model is driving their conclusions, rather than having those conclusions rest on an untested assumption about how errors behave.